bb_log is a fast and tiny logger for Java which only requires one small JAR in your class path. It provides superior performance and is still as simple to use as other loggers, while providing the most commonly used features.

Muse Proxy is a highly customizable multi-platform proxy server. It is easy to use and configure, acting successfully as a gateway to authenticated restricted content, rewriting Web server, Web Access Management, proxy server, and reverse proxy. It has been used for more than 10 years within the Muse Federated Search Platform to manage the authentication to resources and the navigation to full text, by restoring the server session on the end-user browser.

ZedLog is a robust cross-platform input logging tool (A.K.A., a key logger). It is based on a flexible data logging system which makes it easy to get the required data. It features logging of all keyboard and mouse events, a replay simulation tool, logging to a file, and hiding in the background.

libKISSlog is a trivial lightweight C++ template library designed and written according to the KISS (Keep It Simple and Straightforward) principle. It leans heavily on STL for keeping its implementation as simple as its usage, and tries to provide C++ developers with a lightweight, paradigm-pure, and flexible alternative to logging libraries which use design and/or implementation decisions which at least the author of libKISSlog believes to be questionable. Its easiest to explain why libKISSlog would be suitable for your needs by listing the things which libKISSlog does not choose to use or do: no singletons or other forms of mutable global state, no macros, no attempt to fit the Java runtime everything model onto a C++ library, no attempt to be a Java-style (bloated) framework, no attempt to make the choice for you of whether you need thread safety, and no compromise on simplicity in order to facilitate questionable inner-loop logging practices.

LoginIDS provides functions to analyze log files from different services in order to detect unusual login behavior. The normal user behavior is learned by analyzing log files and saved in a database. Logins are analyzed by time, service, source, and destination address. If a user's login is new or considered unlikely by LoginIDS, an alert is generated. Alerts can be handled by external scripts and viewed using the log file management system Splunk and the LoginIDS App.